Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

Abstract:

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

• Periodical: Applied Mechanics and Materials (Volume 740)
• Pages: 871-874
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.740.871
• Online since: March 2015
• Authors: Hui Zhao*, Li Rong Shi, Hong Jun Wang
• Keywords: Bayesian Regularization, BP Neural Network, Principal Component Analysis, Sunlight Greenhouse, Temperature

References

[1] Linlin Qin, Gang Wu . Automation Panorama, 2010(2): 58-60, 64. In Chinese.

[2] Lingli Deng, Baijun Li, Hanping Mao. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2004, 20(1): 263-266. In Chinese.

[3] Guohong Tong, Baoming Li, DavidM. Christopherd, et al. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2007, 23(7): 178-185. In Chinese.

[4] Jiaoliao Chen, Fang Xu, LiBin Zhang et al. Transactions of the Chinese Society for Agricultural Machinery (Transactions of the CSAM), 2008, 39 (8): 114-118. In Chinese.

[5] Yongxiang Jiang, Linlin Qin, Chun Shi, et al. Journal of Jiangnan University (Natural Science Edition). 2013, 12(5): 535-540. In Chinese.